Walmart Labs·Backend Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Walmart Labs backend round, pretty much one meaty algorithm problem about scheduling and room allocation. The follow-up on complexity and testing felt routine but they did push a bit on the two different approaches, which I wasn't fully expecting.

Questions Asked (1)

Given a list of meeting time intervals, find the minimum number of conference rooms needed to accommodate all meetings simultaneously.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight for the min-heap approach: sort by start time, keep a heap of end times, and pop whenever the next meeting can reuse a room.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: intervals are half-open, meetings can start exactly when another ends, and we need the maximum number of overlapping meetings at any point. Then present two solutions: a simple O(n log n) sorting + min-heap approach, and a more efficient O(n log n) sweep line using sorted start and end times. Discuss trade-offs and edge cases.

Pro tip: Mention that the problem is equivalent to finding the maximum overlap, and that a sweep line with two pointers is optimal. Also, proactively discuss how to handle large inputs or streaming data, showing scalability awareness.

1. Clarify requirements and assumptions

Confirm interval semantics (e.g., [start, end) so back-to-back meetings don't conflict), input size, and whether intervals are sorted. Ask if meetings can be modified or if we just need the count.

2. Outline a baseline solution

Sort intervals by start time and use a min-heap to track end times of ongoing meetings. For each meeting, remove ended meetings, then add the new end time; the heap size at any point is the rooms needed.

3. Optimize with sweep line

Separate start and end times into two arrays, sort both, and use two pointers to count concurrent meetings. Increment on start, decrement on end, and track the maximum. This avoids heap overhead and is often faster in practice.

4. Analyze complexity and trade-offs

Both approaches are O(n log n) time and O(n) space. The heap method is more intuitive; the sweep line is more efficient for large n due to lower constant factors. Discuss when to use each.

5. Test with edge cases

Walk through examples: no meetings, one meeting, all overlapping, none overlapping, and back-to-back meetings. Verify that the chosen approach handles them correctly.

Key Points to Mention

The problem reduces to finding the maximum number of overlapping intervals at any time.
Sorting is necessary; O(n log n) is optimal for comparison-based solutions.
Min-heap approach: sort by start, use heap for end times, remove ended meetings before adding new one.
Sweep line approach: sort starts and ends separately, use two pointers to count active meetings.
Edge cases: empty input, single meeting, all meetings overlapping, meetings that touch at endpoints.
Scalability: for very large inputs, consider external sorting or streaming algorithms if data doesn't fit in memory.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.